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20162025
most citedSemantic Instance Segmentation with a Discriminative Loss Function

444 citations · 1.7k across the 90 of their papers we have counts for

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Showing 2019Show all

29 papers · 1 filter

cs.CV2019

Domain Agnostic Feature Learning for Image and Video Based Face Anti-spoofing

Suman Saha, Wenhao Xu, Menelaos Kanakis +4

Nowadays, the increasingly growing number of mobile and computing devices has led to a demand for safer user authentication systems. Face anti-spoofing is a measure towards this di…

cs.CV2019

Self-supervised Object Motion and Depth Estimation from Video

Qi Dai, Vaishakh Patil, Simon Hecker +3

We present a self-supervised learning framework to estimate the individual object motion and monocular depth from video. We model the object motion as a 6 degree-of-freedom rigid-b…

cs.CV2019

Semantic Understanding of Foggy Scenes with Purely Synthetic Data

Martin Hahner, Dengxin Dai, Christos Sakaridis +2

This work addresses the problem of semantic scene understanding under foggy road conditions. Although marked progress has been made in semantic scene understanding over the recent…

cs.CV2019

Talk2Nav: Long-Range Vision-and-Language Navigation with Dual Attention and Spatial Memory

Arun Balajee Vasudevan, Dengxin Dai, Luc Van Gool

The role of robots in society keeps expanding, bringing with it the necessity of interacting and communicating with humans. In order to keep such interaction intuitive, we provide…

cs.CV2019

Extremely Weak Supervised Image-to-Image Translation for Semantic Segmentation

Samarth Shukla, Luc Van Gool, Radu Timofte

Recent advances in generative models and adversarial training have led to a flourishing image-to-image (I2I) translation literature. The current I2I translation approaches require…

cs.AI2019

Talk2Car: Taking Control of Your Self-Driving Car

Thierry Deruyttere, Simon Vandenhende, Dusan Grujicic +2

A long-term goal of artificial intelligence is to have an agent execute commands communicated through natural language. In many cases the commands are grounded in a visual environm…